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, intelligent transportation systems, transportation data analytics, AI/machine learning, Web-based transportation systems, or database development; Strong programming skills in Python, C++, MATLAB, Java
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quantitative field. Good scientific programming skills, particularly in Python, are required. Experience with atmospheric dynamics, numerical modelling, machine learning, or large meteorological datasets would
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of response and develop predictive models. The work will involve analysis of large-scale datasets through multiomics integration, machine learning, statistical genetics, QTL analysis and development of genetic
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. The student will extend a lightweight digital twin for an STM32-based IoT sensing platform by integrating power measurement capabilities, enhancing machine learning models, and evaluating system performance
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on machine learning and networks, release the code in open access and actively participate in the international interpretability community. Where to apply Website https://seuelectronica.upc.edu/en/procedures
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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data. This position provides excellent opportunities to gain research experience in machine learning, medical image analysis, multimodal foundation models, and clinical AI while working in a highly
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/margin metrics used to assess power quality against mission demand. Model generation methodology – develop a methodology for generating reduced-order or behavioural models of the DC bus system directly
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. Preferred Qualifications Strong computational and data analysis skills with experience developing analytical methods. Experience with machine learning, deep learning, graph-based models, variational